System, method, and computer program product for time series forecasting using integrable multivariate pattern matching

Abstract

Provided is a system for time series forecasting, including a computer hardware processor; and at least one non-transitory computer-readable storage medium storing instructions that, when executed by the computer hardware processor, causes the processor to perform: obtaining a target time series; obtaining at least one reference time series associated with the target time series; generating a self-similarity vector for the reference time series by determining a similarity between a current time window of the reference time series and one or more subsequences of the reference time series, different from the current time window; based on the self-similarity vector, identifying one or more historic timestamps of the reference time series with similarity values above a threshold similarity value; generating one or more future projections of the target time series based on the identified timestamps; and generating a forecasting of the target time series using a read-out function.

What is claimed is: 
     
         1 . A system for time series forecasting using a pattern matching-based machine learning model, the system comprising:
 at least one computer hardware processor; and   at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, causes the at least one computer hardware processor to perform a method comprising:
 obtaining a target time series; 
 obtaining at least one reference time series associated with the target time series; 
 generating a self-similarity vector for the reference time series by determining a similarity between a current time window of the reference time series and one or more subsequences of the reference time series, different from the current time window; 
 based on the self-similarity vector, identifying one or more historic timestamps of the reference time series with similarity values above a threshold similarity value; 
 generating one or more future projections of the target time series based on the identified timestamps; and 
 generating a forecasting of the target time series using a read-out function. 
   
     
     
         2 . The system of  claim 1 , wherein the forecasting of the target time series comprises a forecasting of future timestamps of the target time series and a confidence score. 
     
     
         3 . The system of  claim 1 , wherein the forecasting of the target time series comprises a future distribution of the target time series. 
     
     
         4 . The system of  claim 1 , wherein the at least one computer hardware processor is further caused to perform:
 determining whether a decision should be taken based on the forecasting of the target time series.   
     
     
         5 . The system of  claim 1 , wherein obtaining the at least one reference time series comprises:
 transforming the target time series into the at least one reference time series.   
     
     
         6 . The system of  claim 1 , wherein obtaining the at least one reference time series comprises obtaining the at least one reference time series from a data source external to the system, or obtaining the at least one reference time series from storage of the system. 
     
     
         7 . The system of  claim 1 , wherein the at least one reference time series comprises a plurality of reference time series, generating the self-similarity vector comprises generating a respective plurality of self-similarity vectors, and wherein the at least one computer hardware processor is further configured to perform:
 assigning respective weights to each of the plurality self-similarity vectors; and   integrating the plurality of self-similarity vectors using an integration function, wherein the one or more historic timestamps are determined based on the integrated self-similarity vectors.   
     
     
         8 . The system of  claim 1 , wherein the at least one computer hardware processor is further configured to perform:
 training a machine learning model, wherein the training comprises:
 determining one or more historic time points from the target time series; and 
 for each of the one or more historic time points:
 determining respective relevant time points from the target time series, based on a plurality of parameters, wherein each of the relevant time points occur earlier in the time series than the historic time point; 
 comparing a portion of the target time series following the historic time point to a portion of the target time series following each of the relevant time points; and 
 based on the comparing, updating one or more of the plurality of parameters. 
 
   
     
     
         9 . The system of  claim 8 , wherein the plurality of parameters comprises: a subsequence length for generating the self-similarity vector, self-similarity vector weights, and read-out function parameters. 
     
     
         10 . The system of  claim 1 , wherein generating the self-similarity vector comprises:
 for each of the one or more subsequences:
 determining a distance between the current time window and the subsequence; and 
 concatenating the distance into a self-similarity vector. 
   
     
     
         11 . A method for time series forecasting using a pattern matching-based machine learning model, the method comprising:
 using at least one computer hardware processor to perform:
 obtaining a target time series; 
 obtaining at least one reference time series associated with the target time series; 
 generating a self-similarity vector for the reference time series by determining a similarity between a current time window of the reference time series and one or more subsequences of the reference time series, different from the current time window; 
 based on the self-similarity vector, identifying one or more historic timestamps of the reference time series with similarity values above a threshold similarity value; 
 generating one or more future projections of the target time series based on the identified timestamps; and 
 generating a forecasting of the target time series using a read-out function. 
   
     
     
         12 . The method of  claim 11 , wherein obtaining the at least one reference time series comprises:
 transforming the target time series into the at least one reference time series, obtaining the at least one reference time series from a data source external to a system containing the at least one computer hardware processor, or obtaining the at least one reference time series from storage of the system.   
     
     
         13 . The method of  claim 11 , wherein the at least one reference time series comprises a plurality of reference time series, generating the self-similarity vector comprises generating a respective plurality of self-similarity vectors, and further comprising:
 assigning respective weights to each of the plurality self-similarity vectors; and   integrating the plurality of self-similarity vectors using an integration function, wherein the one or more historic timestamps are determined based on the integrated self-similarity vectors.   
     
     
         14 . The method of  claim 11 , further comprising:
 training a machine learning model, wherein the training comprises:
 determining one or more historic time points from the target time series; and 
 for each of the one or more historic time points:
 determining respective relevant time points from the target time series, based on a plurality of parameters, wherein each of the relevant time points occur earlier in the time series than the historic time point; 
 comparing a portion of the target time series following the historic time point to a portion of the target time series following each of the relevant time points; and 
 based on the comparing, updating one or more of the plurality of parameters. 
 
   
     
     
         15 . The method of  claim 14 , wherein the plurality of parameters comprises: a subsequence length for generating the self-similarity vector, self-similarity vector weights, and read-out function parameters. 
     
     
         16 . The method of  claim 11 , wherein generating the self-similarity vector comprises:
 for each of the one or more subsequences:
 determining a distance between the current time window and the subsequence; and 
 concatenating the distance into a self-similarity vector. 
   
     
     
         17 . At least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by at least one computer hardware processor, causes the at least one computer hardware processor to perform a method comprising:
 obtaining a target time series;   obtaining at least one reference time series associated with the target time series;   generating a self-similarity vector for the reference time series by determining a similarity between a current time window of the reference time series and one or more subsequences of the reference time series, different from the current time window;   based on the self-similarity vector, identifying one or more historic timestamps of the reference time series with similarity values above a threshold similarity value;   generating one or more future projections of the target time series based on the identified timestamps; and   generating a forecasting of the target time series using a read-out function.   
     
     
         18 . The non-transitory computer-readable storage medium of  claim 17 , wherein obtaining the at least one reference time series comprises:
 transforming the target time series into the at least one reference time series, obtaining the at least one reference time series from a data source external to a system containing the at least one computer hardware processor, or obtaining the at least one reference time series from storage of the system.   
     
     
         19 . The non-transitory computer-readable storage medium of  claim 17 , wherein the at least one reference time series comprises a plurality of reference time series, generating the self-similarity vector comprises generating a respective plurality of self-similarity vectors, and wherein the method further comprises:
 assigning respective weights to each of the plurality self-similarity vectors; and   integrating the plurality of self-similarity vectors using an integration function, wherein the one or more historic timestamps are determined based on the integrated self-similarity vectors.   
     
     
         20 . The non-transitory computer-readable storage medium of  claim 17 , wherein the method further comprises:
 training a machine learning model, wherein the training comprises:
 determining one or more historic time points from the target time series; and 
 for each of the one or more historic time points:
 determining respective relevant time points from the target time series, based on a plurality of parameters, wherein each of the relevant time points occur earlier in the time series than the historic time point; 
 comparing a portion of the target time series following the historic time point to a portion of the target time series following each of the relevant time points; and 
 based on the comparing, updating one or more of the plurality of parameters, wherein the plurality of parameters comprises: a subsequence length for generating the self-similarity vector, self-similarity vector weights, and read-out function parameters.